Diaa M. Uliyan

A novel forged blurred region detection system for image forensic applications

As new technologies and devices are introduced in the market, the crime rate also increases in developing and developed countries. One such crime is image forgery which can be detected by forensic applications. In this paper, we propose a novel idea for identifying forgery attack done by blur artifact unlike existing forgery attack done by geometrical distortion such as rotation and scaling. The proposed method segment region of interest from the input forgery image based on the combination of statistical analysis with color texture analysis which includes blur artifact region. For each region of interest, we propose a new method for estimating degree of blur to separate forged blur artifact and normal blur artifact. In order to validate the identified forged blur artifact, we explore Fourier and Gabor texture features to study the structure of the forged blur artifact which eliminates false blur forged blur artifact. To evaluate the proposed forged blurred region detection method, we use two standard databases namely, Image data manipulation, and MICC-F220 for experimentation. Experimental results of the proposed method with existing methods show that the proposed method outperforms the existing methods in terms of forged blur artifact region detection.

Copy move image forgery detection using Hessian and center symmetric local binary pattern

Region duplication has become common in image forgery owing to the availability of advanced editing software and fully equipped digital cameras. Most existing block-based copy-move detection techniques struggle to detect such tampering under postprocessing operations, such as scaling and JPEG compression. This study proposes a copy-move image forgery detection algorithm using Hessian features and a center-symmetric local binary pattern (CSLBP). The proposed method consists of four steps: (1) detecting the object based on normalized cut segmentation, (2) localizing the local interest points of each object based on the Hessian method, (3) extracting CSLBP features, and (4) detecting duplicated regions in image forgeries. Experiment results show that the method is robust to postprocessed copy-move forgery under scaling, and JPEG compression.

Image Region Duplication Forgery Detection Based on Angular Radial Partitioning and Harris Key-Points

Region duplication forgery where a part of the image itself is copied and pasted onto a different part of the same image grid is becoming more popular in image manipulation. The forgers often apply geometric transformations such as rotation and scaling operations to make the forgery imperceptible. In this study, an image region duplication forgery detection algorithm is proposed based on the angular radial partitioning and Harris key-points. Two standard databases have been used: image data manipulation and MICC-F220 (Media Integration and Communication Center– of the University of Florence) for experimentation. Experiment results demonstrate that the proposed technique can detect rotated regions in multiples of 30 degrees and can detect region duplication with different scaling factors from 0.8, to 1.2. More experimental results are presented to confirm the effectiveness of detecting region duplication that has undergone other changes, such as Gaussian noise, and JPEG compression.

Electromagnetismlike Mechanism Descriptor with Fourier Transform for a Passive Copy-move Forgery Detection in Digital Image Forensics.

Copy-move forgery is a special type of forgery that involves duplicating one region of an image by covering it with a copy of another region from the same image. This study develops a simple and powerful descriptor called Electromagnetismlike mechanism descriptor (EMag), for locating tampered areas in copy-move forgery on the basis of Fourier transform within a reasonable amount of time. EMag is based on the collective attraction-repulsion mechanism, which considers each images pixel as an electrical charge. The main component of EMag is the degree of the attraction-repulsion force between the current pixel and its neighbours. In the proposed algorithm, the image is divided into similar non-overlapping blocks, and then the final force for each block is evaluated and used to construct the tampered image features vector. The experimental results demonstrate the efficiency of the proposed algorithm in terms of detection time and detection accuracy. The detection rate of the proposed algorithm is improved by reduction of false positive rate (FPR) and increment of true positive rate (TPR).

A Survey on Internet of Things Enabled Smart Campus Applications

The fictional future home, workspace or city, as predicted by science TV shows of the 1960s, is now a reality. Modern microelectronics and communication technologies offer the type of smart living that looked practically inconceivable just a few decades ago. The Internet of Things (IoT) is one of the main drivers of the future smart spaces. It enables new operational technologies and offers vital financial and environmental benefits. With IoT, spaces are evolving from being just smart to become intelligent and connected. This survey paper focuses on how to leverage IoT technologies to build a modular approach to smart campuses. The paper identifies the key benefits and motivation behind the development of IoT-enabled campus. Then, it provides a comprehensive view of general types of smart campus applications. Finally, we consider the vital design challenges that has to be met to realise a smart campus.

State of the art in passive digital image forgery detection: copy-move image forgery

Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks.

Segmented-Based Region Duplication Forgery Detection Using MOD Keypoints and Texture Descriptor

Nowadays, with a rapid development of digital image technology, image forgery is made easy. Image forgery has considerable consequences, e.g., medical images, miscarriage of justice, political, etc. For instance, in digital newspapers, forged images will mislead public opinion and falsify the truth. In this paper, we proposed a segmentation-based region duplication forgery detection method, by extracting Maximization of Distinctiveness (MOD) keypoints for matching from segmented regions in the image. The main challenge is when the duplicated regions have been affected by rotation and scaling attacks. As a result, the proposed method detects duplicated regions based on two stages, structure analysis and texture analysis. In the first stage, the doubtful image is segmented into regions using the K-means algorithm. The segmented regions then labeled by centroids and MOD keypoints to represent their internal structures. MOD detects local interest points that are robust to rotation and improve detection rate in term of True Positive Rate (TPR). In the second stage, in order to identify the validated forged region, we explore Multiobjective Gradient Operator (MO-GP) to study the internal texture of segmented regions and eliminate the False Positive Rate (FPR) of forged regions. Experiment results show that our method can detect region duplication forgery under rotation, blurring and noise addition for JPEG images on MICC-F220 dataset with average TPR = 93% and FPR = 2%.

A Shared Memory Method For Enhancing The HTNGH AlgorithmPerformance: Proposed Method

In bioinformatics, pair-wise alignment plays a significant role insequence comparison by rating the similarities and distances between protein, DeoxyriboNucleic Acid (DNA), and RiboNucleic Acid (RNA) sequences. Sequence comparison considered as a key stone in building distance matrices. Due to the rapid growth of molecular databases, the need for faster sequence comparison and alignment has become anecessity. High performance computing impacthas increased in the last decade through providing many high performance architectures and tools. In this paper we present a parallel shared memory design for a dynamic programming algorithm named Hash Table-N-Gram-Hirschberg (HT-NGH) an extension of Hashing-N-Gram-Hirschberg (HNGH) and N-Gram-Hirschberg (NGH) algorithm, to speed up the sequence alignment construction process. The focus of the proposed method ison the transformation phase of HT-NGH algorithm since it takes10% of HT-NGH overall run time. The experimental evaluation of the proposed parallel designshows an enhancement in the execution time and speedup without sacrificing the accuracy. However, the decomposition method might slightly slowdown the proposed algorithm due to the differences in performance between the processing units.

Image Encryption Technique based on the Entropy Value of a Random Block

The use of digital images in most fields of information technology systems makes these images usually contain confidential information. When these images transmitted via the Internet especially in the Cloud, it becomes necessary to protect these images in a way that ensure putting the confidential information that are contained far away from the attackers. A proposed image encryption technique has been presented in this work. This technique used a secret key that is extracted from the image content itself. Therefore, there is no need to find a secret channel to exchange any key where, sender and receiver authenticate each other with regards to a shared secret key extracted from the image. The technique constructs its secret key that is used to encrypt the image, based on the entropy values of a set of randomly selected blocks from the image itself. Vairous experiments have been conducted to evaluate the strength and performance of the technique. The experimental results shows that the proposed technique can be used effectively in the field of image security to protect and authenticate images.

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

Due to the availability of powerful image editing softwares, forgers can tamper the image content easily. There are various types of image forgery, such as image splicing and region duplication forgery. Region duplication is one of the most common manipulations used for tampering digital images. It is vital in image forensics to authenticate the digital image. In this paper, a novel region duplication forgery detection approach is proposed. By segmenting the input image based on the colour features, sufficient number of centroids are produced, that exist even in small or smooth regions. Then, the Least Significant Bit (LSB) of all the colours of pixels in each segment are extracted to build the signature vector. Finally, the hamming distance is calculated through exploiting the signature vector of image to find the dissimilarity. Various experimental results are provided to demonstrate the superior performance of the proposed scheme under some post processing operations such as scaling attack.